Agentic AI Architect Job New York New York USA,Software Development

Location: New York

Define end-to-end architecture for agentic AI-enabled platform across data, AI, orchestration, and integration layers
Design and govern agentic orchestration framework for multi-step workflows
Establish architecture patterns for - RAG and grounding, Vector search and retrieval, MCP tool access layer, prompt management and evaluation

Platform & Integration Design

Define integration architecture across
- Lakehouse, ODS, document systems;
Underwriting systems and third-party APIs
Design configurable, metadata-driven framework for multi-LOB onboarding

AI & GenAI Enablement

Define where and how to use
- GenAI vs deterministic logic, agentic workflows vs pipeline workflows
Establish multimodal integration approach combining structured, unstructured, and external data
Design prompt lifecycle, evaluation, and optimization strategy

Governance, Safety & Model Ops

Define AI safety and guardrails (PII, hallucination control, policy constraints)
Establish Model Ops and Prompt Ops frameworks
Ensure explainability, auditability, and traceability of AI outputs
Lead technical execution across AI, data, and platform teams
Guide engineers (AI, data, full-stack) and ensure alignment with architecture
Drive technical decisions and stakeholder communication

Candidate Profile:

Experience
: 10–15+ years in software/data/AI engineering with 4–6+ years in AI/ML/GenAI architecture
Background
:
Strong experience in designing enterprise-scale platforms and distributed systems
Domain (good to have):
Insurance / reinsurance / financial services
Education
:
Bachelor's or Master's in Computer Science, Engineering, Data Science, or related field
Profile Type
:
Hands-on architect with ability to balance strategy + execution

Technical skills:

Retrieval & RAG - Azure AI Search (indexing, vector search, hybrid search);
Embedding pipelines and retrieval optimization; RAG design, grounding strategies, context management
Tool Access & Integration - MCP (Model Context Protocol) architecture and tool design; API design (FastAPI / REST / microservices);
Integration with enterprise systems and third-party APIs
AI Safety & Governance - NVIDIA NeMo Guardrails;

Microsoft Presidio (PII detection/masking);
Guardrails for prompt injection, hallucination control
Evaluation & Model Ops - Azure AI Foundry (model hosting, versioning, monitoring);
Evaluation frameworks (LLM-as-judge, test datasets);
Prompt/version control, cost/latency monitoring
Dev Ops & Observability - CI/CD pipelines (Azure Dev Ops / Git Hub Actions);
Logging, monitoring, observability (App Insights, etc.);
Performance tuning and scalability

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